What is a goal-seeking agent?

A goal-seeking agent is an autonomous AI system that focuses on achieving a high-level outcome by planning and adapting its own steps rather than following a rigid, predefined script.

Does this skill work with Kubernetes or Cloud environments?

Yes, it includes specific architectural guidance and real-world examples for complex environments like Azure Kubernetes Service (AKS) and SRE automation.

When should I use this pattern instead of a standard script?

Use this pattern when the problem space is large, runtime context varies significantly, or you need the agent to autonomously recover from failures without human intervention.

How does it handle multi-phase tasks?

The pattern provides a framework to decompose complex goals into manageable phases with defined dependencies, allowing the agent to evaluate progress and success at each stage.

Goal-Seeking Agent Pattern

Name: Goal-Seeking Agent Pattern
Author: rysweet

byrysweet

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Productividad y Flujo de Trabajo

Empowers architects to design and implement autonomous, outcome-oriented agents for complex multi-phase workflows.

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The Goal-Seeking Agent Pattern skill provides a comprehensive framework for building autonomous AI agents that prioritize objectives over rigid procedural steps. It guides software architects through the process of analyzing complex workflows, determining if an autonomous approach is appropriate, and structuring multi-phase execution plans. By leveraging this pattern, developers can create resilient, self-correcting agents capable of handling environmental variability in domains like SRE automation, CI/CD diagnostics, and adaptive system fixing, ensuring high-level goals are met even when the path to success is non-deterministic.

Características Principales

Self-directed failure recovery and alternative path exploration
16 GitHub stars
Objective-oriented success criteria and self-assessment logic
Standardized integration patterns for goal-seeking generators
Autonomous multi-phase execution planning and dependency mapping
Context-aware strategy selection for adaptive decision making

Casos de Uso

Creating adaptive code-fixing agents that vary diagnostic depth based on issue complexity
Automating complex SRE tasks such as AKS cluster health remediation
Developing self-healing CI/CD pipelines that autonomously diagnose and fix test failures

Acerca de

Características Principales

Self-directed failure recovery and alternative path exploration
16 GitHub stars
Objective-oriented success criteria and self-assessment logic
Standardized integration patterns for goal-seeking generators
Autonomous multi-phase execution planning and dependency mapping
Context-aware strategy selection for adaptive decision making

Casos de Uso

Creating adaptive code-fixing agents that vary diagnostic depth based on issue complexity
Automating complex SRE tasks such as AKS cluster health remediation
Developing self-healing CI/CD pipelines that autonomously diagnose and fix test failures